Advertisement

Attention, Perception, & Psychophysics

, Volume 76, Issue 8, pp 2485–2494 | Cite as

Improving myopia via perceptual learning: is training with lateral masking the only (or the most) efficacious technique?

  • Rebecca CamilleriEmail author
  • Andrea Pavan
  • Filippo Ghin
  • Gianluca CampanaEmail author
Article

Abstract

Perceptual learning produces an improvement in visual functions such as an increase in visual acuity (VA) and contrast sensitivity in participants with both amblyopia and refractive defects. This improvement has been observed in the presence of lateral masking, which is known to bring about lateral interactions between detectors in early cortical pathways. Improvement has also been revealed in the absence of flankers in healthy individuals and those with amblyopia. This study seeks to understand whether a perceptual training regime really needs to be based on lateral interactions in cases where poor vision is not due to cortical dysfunction, such as myopia. Ten participants with mild myopia (max –2D) were recruited. A battery of tests measuring visual function was administered prior to (pre-test) and following (post-test) the training. The participants carried out an 8-week behavioural training using a single Gabor perceptual learning paradigm, completing a total of 24 sessions. Results indicate that training using a single Gabor protocol results in a VA improvement of 0.16 logMAR. The present study supports the idea that, in the absence of cortical deficits, as is the case in myopia, some sort of compensatory mechanism can take place at the cortical level by means of perceptual learning, resulting in more effective processing of the received blurred input. However, regarding training based on lateral masking, here we found that improvement of visual functions was smaller and limited to VA. This might suggest that training based on lateral masking, which is able to modify the strength of facilitatory and inhibitory lateral interactions, could be more effective for optimal recovery of blurred vision.

Keywords

Visual perceptual learning Myopia Lateral interactions Visual acuity Contrast sensitivity 

Notes

Acknowledgements

We would like to thank Stefano Cappello (optician/optometrist, Padova, Italy) for the optometric assessment of the participants as well as Silvia Rigoni for helping out with data collection.

References

  1. Adini, Y., Sagi, D., & Tsodyks, M. (2002). Context-enabled learning in the human visual system. Nature, 415(6873), 790–793.PubMedCrossRefGoogle Scholar
  2. Adini, Y., Wilkonsky, A., Haspel, R., Tsodyks, M., & Sagi, D. (2004). Perceptual learning in contrast discrimination: The effect of contrast uncertainty. Journal of Vision, 4(12), 993–1005.PubMedCrossRefGoogle Scholar
  3. Ahissar, M., & Hochstein, S. (1993). Attentional control of early perceptual learning. Proceedings of the National Academy of Sciences of the United States of America, 90(12), 5718–5722.PubMedCentralPubMedCrossRefGoogle Scholar
  4. Ahissar, M., & Hochstein, S. (1996). Learning pop-out detection: Specificities to stimulus characteristics. Vision Research, 36(21), 3487–3500.PubMedCrossRefGoogle Scholar
  5. Ahissar, M., & Hochstein, S. (1997). Task difficulty and the specificity of perceptual learning. Nature, 387(6631), 401–406.PubMedCrossRefGoogle Scholar
  6. Bach, M. (1996). The “Freiburg Visual Acuity Test”—automatic measurement of visual acuity. Optometry and Vision Science, 73, 49–53.PubMedCrossRefGoogle Scholar
  7. Bejjanki, V. R., Beck, J. M., Lu, Z. L., & Pouget, A. (2011). Perceptual learning as improved probabilistic inference in early sensory areas. Nature Neuroscience, 14(5), 642–648.PubMedCentralPubMedCrossRefGoogle Scholar
  8. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 443–446.CrossRefGoogle Scholar
  9. Campana, G., & Casco, C. (2003). Learning in combined-feature search: Specificity to orientation. Perception & Psychophysics, 65(8), 1197–1207.CrossRefGoogle Scholar
  10. Casco, C., Campana, G., Grieco, A., & Fuggetta, G. (2004). Perceptual learning modulates electrophysiological and psychophysical response to visual texture segmentation in humans. Neuroscience Letters, 371(1), 18–23.PubMedCrossRefGoogle Scholar
  11. Chung, S. T., Li, R. W., & Levi, D. M. (2006). Identification of contrast-defined letters benefits from perceptual learning in adults with amblyopia. Vision Research, 46(22), 3853–3861.PubMedCentralPubMedCrossRefGoogle Scholar
  12. Chung, S. T., Li, R. W., & Levi, D. M. (2008). Learning to identify near-threshold luminance-defined and contrast-defined letters in observers with Amblyopia. Vision Research, 48, 2739–2750.PubMedCentralPubMedCrossRefGoogle Scholar
  13. Durrie, D., & McMinn, S. P. (2007). Computer-based primary visual cortex training for treatment of low myopia and early presbyopia. Transactions of the American Ophthalmological Society, 105, 132–140.PubMedCentralPubMedGoogle Scholar
  14. Fahle, M., & Henke-Fahle, S. (1996). Interobserver variance in perceptual performance and learning. Investigative Ophthalmology and Visual Science, 37(5), 869–877.PubMedGoogle Scholar
  15. Fahle, M., & Poggio, T. (2002). Perceptual learning. Cambridge, Masssachusetts: MIT Press.Google Scholar
  16. Fiorentini, A., & Berardi, N. (1980). Perceptual learning specific for orientation and spatial frequency. Nature, 287(5777), 43–44.PubMedCrossRefGoogle Scholar
  17. Gilbert, C. D., Sigman, M., & Crist, R. E. (2001). The neural basis of perceptual learning. Neuron, 31(5), 681–697.PubMedCrossRefGoogle Scholar
  18. Huang, C. B., Lu, Z. L., & Zhou, Y. (2009). Mechanisms underlying perceptual learning of contrast detection in adults with anisometropic amblyopia. Journal of Vision, 9(11), 1–14.Google Scholar
  19. Huang, C. B., Zhou, Y., & Lu, Z. L. (2008). Broad bandwidth of perceptual learning in the visual system of adults with anisometropic amblyopia. Proceedings of the National Academy of Sciences of the United States of America, 105(10), 4068–4073.PubMedCentralPubMedCrossRefGoogle Scholar
  20. Karni, A., & Sagi, D. (1991). Where practice makes perfect n texture discrimination: Evidence for primary visual cortex plasticity. Proceedings of the National Academy of Sciences of the United States of America, 88, 4966–4970.PubMedCentralPubMedCrossRefGoogle Scholar
  21. Levi, D. M. (2005). Perceptual learning in adults with amblyopia: A re-evaluation of critical periods in human vision. Developmental Psychobiology, 46(3), 222–232.PubMedCrossRefGoogle Scholar
  22. Levi, D. M., & Li, R. W. (2009). Perceptual learning as a potential treatment for amblyopia. Vision Research, 21, 2535–2549.CrossRefGoogle Scholar
  23. Levitt, H. (1971). Transformed up-down methods in psychoacoustics. Journal of the Acoustical Society of America, 49(2), 467–477.Google Scholar
  24. Li, R. W., & Levi, D. M. (2004). Characterizing the mechanisms of improvement for position discrimination in adult amblyopia. Journal of Vision, 4, 476–487.PubMedCrossRefGoogle Scholar
  25. Lu, Z. L., & Dosher, B. A. (2004). Spatial attention excludes external noise without changing the spatial frequency tuning of the perceptual template. Journal of Vision, 4(10), 955–966.PubMedCrossRefGoogle Scholar
  26. Maniglia, M., Pavan, A., Cuturi, L. F., Campana, G., Sato, G., & Casco, C. (2011). Reducing crowding by weakening inhibitory lateral interactions in the periphery with perceptual learning. PLoS One, 6(10), e25568.PubMedCentralPubMedCrossRefGoogle Scholar
  27. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision, 10, 437–442.PubMedCrossRefGoogle Scholar
  28. Polat, U. (1999). Functional architecture of long-range perceptual interactions. Spatial Vision, 12, 143–162.PubMedCrossRefGoogle Scholar
  29. Polat, U. (2009). Making perceptual learning practical to improve visual functions. Vision Research, 49, 2566–2573.Google Scholar
  30. Polat, U., Ma-Naim, T., Belkin, M., & Sagi, D. (2004). Improving vision in adult amblyopia by perceptual learning. Proceedings of the National Academy of Sciences of the United States of America, 101(17), 6692–6697.PubMedCentralPubMedCrossRefGoogle Scholar
  31. Polat, U., Ma-Naim, T., & Spierer, A. (2009). Treatment of children with amblyopia by perceptual learning. Vision Research, 49(21), 2599–2603.PubMedCrossRefGoogle Scholar
  32. Polat, U., & Sagi, D. (1993). Lateral interactions between spatial channels: Suppression and facilitation revealed by lateral masking experiments. Vision Research, 33(7), 993–999.PubMedCrossRefGoogle Scholar
  33. Polat, U., & Sagi, D. (1994a). The architecture of perceptual spatial interactions. Vision Research, 34(1), 73–78.PubMedCrossRefGoogle Scholar
  34. Polat, U., & Sagi, D. (1994b). Spatial interactions in human vision: From near to far via experience-dependant cascades of connections. Proceedings of the National Academy of Sciences of the United States of America, 91(4), 1206–1209.PubMedCentralPubMedCrossRefGoogle Scholar
  35. Polat, U., & Sagi, D. (2006). Temporal asymmetry of collinear interactions. Vision Research, 46, 953–960.PubMedCrossRefGoogle Scholar
  36. Polat, U., Schor, C., Tong, J. L., Zomet, A., Lev, M., Yehezkel, O., ... Levi, D. M. (2012). Training the brain to overcome the effect of aging on the human eye. Scientific Reports, 2, 278.PubMedCentralPubMedCrossRefGoogle Scholar
  37. Pourtois, G., Rauss, K. S., Vuilleumier, P., & Schwartz, S. (2008). Effects of perceptual learning on primary visual cortex activity in humans. Vision Research, 48(1), 55–62.PubMedCrossRefGoogle Scholar
  38. Rosa, A. M., Silva, M. F., Ferreira, S., Murta, J., & Castelo-Branco, M. (2013). Plasticity in the human visual cortex: An ophthalmology-based perspective. BioMedical Research International, ID 568354, 13.Google Scholar
  39. Saarinen, J., & Levi, D. M. (1995). Perceptual learning in Vernier acuity: What is learned? Vision Research, 35(4), 519–527.PubMedCrossRefGoogle Scholar
  40. Sagi, D. (2011). Perceptual learning in Vision Research. Vision Research, 51, 1552–1566.PubMedCrossRefGoogle Scholar
  41. Schwabe, L., & Obermayer, K. (2005). Learning in top-down gain control of feature selectivity in a recurrent network model of a visual cortical area. Vision Research, 45(25–26), 3202–3209.PubMedCrossRefGoogle Scholar
  42. Schwartz, S., Maquet, P., & Frith, C. (2002). Neural correlates of perceptual learning: A functional MRI study of visual texture discrimination. Proceedings of the National Academy of Sciences of the United States of America, 99(26), 17137–17142.PubMedCentralPubMedCrossRefGoogle Scholar
  43. Swift, D. J., & Smith, R. A. (1983). Spatial frequency masking and Weber’s law. Vision Research, 23(5), 495–505.PubMedCrossRefGoogle Scholar
  44. Tan, D. T., & Fong, A. (2008). Efficacy of neural vision therapy to enhance CS and visual acuity in low myopia. Journal of Cataract and Refractive Surgery, 34(4), 570–577.PubMedCrossRefGoogle Scholar
  45. Tanaka, Y., & Sagi, D. (1998). Long-lasting, long-range detection facilitation. Vision Research, 38, 2591–2599.PubMedCrossRefGoogle Scholar
  46. Webster, M. A., Georgeson, M. A., & Webster, S. M. (2002). Neural adjustments to image blur. Nature Neuroscience, 5, 839–840.PubMedCrossRefGoogle Scholar
  47. Yalcin, E., & Balci, O. (2013). Efficacy of perceptual vision therapy in enhancing visual acuity and contrast sensitivity function in adult hypermetropic anisometropic amblyopia. Clinical Ophthalmology, 8, 49–53.Google Scholar
  48. Yu, C., Klein, S. A., & Levi, D. M. (2004). Perceptual learning in contrast discrimination and the (minimal) role of context. Journal of Vision, 4(3), 169–182.PubMedCrossRefGoogle Scholar
  49. Zhang, J. Y., Cong, L. J., Levi, D. M., Klein, S. A., & Yu, C. (2014). Perceptual learning improves adult amblyopic vision through rule-based cognitive compensation. Investigative Ophthalmology and Visual Science, 55(4), 2020–2030.PubMedCentralPubMedCrossRefGoogle Scholar
  50. Zhou, Y., Huang, C., Xu, P., Tao, L., Qiu, Z., Li, X., & Lu, Z. L. (2006). Perceptual learning improves CS and VA in adults with anisometropic amblyopia. Vision Research, 46(5), 739–750.PubMedCrossRefGoogle Scholar
  51. Zhou, J., Zhang, Y., Dai, Y., Zhao, H., Li, R., Hou, F., ... Zhou, Z. (2012). The eye limits the brain's learning potential. Nature Scientific Reports, 364, 1–6.Google Scholar

Copyright information

© The Psychonomic Society, Inc. 2014

Authors and Affiliations

  1. 1.Department of General PsychologyUniversity of PadovaPadovaItaly
  2. 2.Institute for Experimental PsychologyUniversity of RegensburgRegensburgGermany
  3. 3.Human Inspired Technologies Research Center (HIT)University of PadovaPadovaItaly

Personalised recommendations